2. Joseph K. Berry CSU Alumnus, MS in Business Management ’72 and PhD emphasizing Remote Sensing ‘76 W.M. Keck Scholar in Geosciences, University of Denver Principal, Berry & Associates // Spatial Information Systems 1701 Lindenwood Drive, Fort Collins, CO 80524 Phone: (970) 215-0825 Email: jberry@innovativegis.com Website at www.innovativegis.com/basis Geotechnology Not Your Grandfather’s Map

3. (Nanotechnology) Geotechnology (Biotechnology) Global Positioning System Remote Sensing Geographic Information Systems Where is What GPS/GIS/RS Analysis involves investigation of spatial relationships (numerical)PrescriptiveModeling Mapping involves precise placement (delineation) of physical features (graphic)DescriptiveMapping Geotechnology is one of the three "mega technologies" for the 21st century and promises to forever change how we conceptualize, utilize and visualize spatial relationships in scientific research and commercial applications (Berry)

4. (Nanotechnology) Geotechnology (Biotechnology) Internet Mapping “The Sizzle” DescriptiveMapping GPS Navigation Desktop MappingMultimedia Mapping … we created a multimedia map of Pingree Park Last Year(Berry)

5. (Nanotechnology) Geotechnology (Biotechnology) Surface Modeling maps the spatial distribution and pattern of point data… Map Generalization— characterizes spatial trends (tilted plane) Spatial Interpolation— deriving spatial distributions (e.g. IDW, Krig) Other— roving windows and facets (e.g., density surface; tessellation) Spatial Data Mining investigates the “numerical” relationships in mapped data… Descriptive— aggregate statistics (e.g. average, stdev, similarity; clustering) Predictive— relationships among maps (e.g., regression) Prescription— appropriate actions (e.g., decision rules; optimization) Spatial Analysis investigates the “contextual” relationships in mapped data… Reclassify— reassigns map values (position, value, size, shape, contiguity) Overlay— map coincidence (point-by-point; region-wide; map-wide) Distance— proximity and connection (movement; optimal paths; visibility) Neighbors— roving windows (slope; aspect; diversity; anomaly) Grid-Based Map Analysis “The Science” PrescriptiveModeling Spatial Statistics(Berry)

6. Surface Modeling (Density Surface — “counts”) (Berry) Elevation …continuous Map Surface (grid-based analysis frame) A value is stored at each grid cell location indicates “what is where”— for example, a set of elevation values form the familiar terrain surface we hike on. A paradigm shift from traditional discrete Map Features comprised of Points, Lines, Polygons. …Map Surfaces are used to investigate relationships within and among map layers Map Stack Hugag Density Surface Hugag Activity draped over Elevation Continuous Map Surface Avg- 17.49 StDev= 14.99 Most of the activity is on the NE ridge in cover type 14 near steep slopes toward the river HugagCounts Discrete Map Surface 2 Hugags every 30 min for 30 days Hugag

7. Surface Modeling (Mapping the Variance) The “iterative smoothing” process is similar to slapping a big chunk of modeler’s clay over the “data spikes,” then taking a knife and cutting away the excess to leave a continuous surface that encapsulates the Peaks and valleys implied in the field samples – Spatial Distribution Continuous Surface — Geographic Distribution Numeric Distribution — Average, Standard Deviation (Berry)

8. Spatial Interpolation (soil nutrient levels) Spatial Interpolation maps the geographic distribution inherent in the data IDW SurfaceData “Spikes” Corn Field Phosphorous (P) (Berry)

9. Comparing Spatial Interpolation Results Comparison of the IDW interpolated surface to the whole field average shows large differences in localized estimates (-16.6 to 80.4 ppm) Comparison of the IDW interpolated surface to the Krig interpolated surface shows small differences in localized estimates (-13.3 to 11.7 ppm) (-13.3 to 11.7 ppm) (Berry)

10. Spatial Data Mining What spatial relationships do you see? Interpolated Spatial Distribution Phosphorous (P) …do relatively high levels of P often occur with high levels of K and N? …how often? …where? …how often? …where? HUMANS can “see” broad generalized patterns in a single map variable (Berry)

11. Clustering Maps for Data Zones …groups of “floating balls” in data space identify locations in the field with similar data patterns– data zones COMPUTERS can “see” detailed patterns in multiple map variables(Berry)

12. The Precision Ag Process (Fertility example) As a combine moves through a field 1) it uses GPS to check its location then 2) checks the yield at that location to 3) create a continuous map of the yield variation every few feet (dependent map variable). On-the-Fly Yield Map Steps 1–3) Derived Nutrient Maps Step 4) Prescription Map Zone 3 Zone 2 Zone 1 The yield map 4) is analyzed in combination with soil, terrain and other maps (independent map variables) to derive a “Prescription Map” … Variable Rate Application Step 5) 5) …that is used to adjust fertilization levels every few feet in the field (action). Intelligent Implements “As-applied” maps …more generally termed the Spatial Data Mining Process (e.g., Geo-Business application) (Berry)

13. Continuous Spatial Distribution (Detailed) Map Analysis Spatially Interpolated data (Geographic Space — Spatial Statistics) Data Analysis Perspectives (review) (Data vs. Geographic Space) Identifies the Central Tendency Maps the Variance Central Tendency Average = 22.0 StDev = 18.7 Typical How Typical Discrete Spatial Object (Generalized) 22.028.2 Traditional Analysis Field Data Standard Normal Curve fit to the data (Data Space — Non-spatial Statistics) (Berry)

14. Precision Ag (Individual Field Focus) Terrain Soils Yield Potassium CIR Image Precision Conservation (compared to Precision Ag) Isolated Perspective 2-dimensional Precision Conservation (Farm, Watershed,… Focus) Wind Erosion Runoff Leaching Soil Erosion Chemicals (Stewardship Focus)(Production Focus) Interconnected Perspective 3-dimensional (Berry)

15. (Nanotechnology) Geotechnology (Biotechnology) Surface Modeling maps the spatial distribution and pattern of point data… Map Generalization— characterizes spatial trends (tilted plane) Spatial Interpolation— deriving spatial distributions (e.g. IDW, Krig) Other— roving windows and facets (e.g., density surface; tessellation) Spatial Data Mining investigates the “numerical” relationships in mapped data… Descriptive— aggregate statistics (e.g. average, stdev, similarity; clustering) Predictive— relationships among maps (e.g., regression) Prescription— appropriate actions (e.g., decision rules; optimization) Spatial Analysis investigates the “contextual” relationships in mapped data… Reclassify— reassigns map values (position, value, size, shape, contiguity) Overlay— map coincidence (point-by-point; region-wide; map-wide) Distance— proximity and connection (movement; optimal paths; visibility) Neighbors— roving windows (slope; aspect; diversity; anomaly) Grid-Based Map Analysis Spatial Statistics Spatial Analysis (Berry) “The Science” PrescriptiveModeling

16. Slopemap …relative terrain steepness Flowmap …relative amount of water Elevation …continuous Map Surface (grid-based analysis frame) Spatial Analysis (example procedures) (Berry)(Berry) Simple Proximity to Roads …far from Roads Roads & Water Viewshed from Roads …not seen Visual Exposure from Roads …seen a lot …whereas Spatial Statistics investigates Numerical Relationships, Spatial Analysis investigates Geographic Context Map Stack

17. Calculating Slope and Flow (terrain analysis) Elevation Surface Inclination of a fitted plane to a location and its eight surrounding elevation values Total number of the steepest downhill paths flowing into each location Slope (47,64) = 33.23% Slope map draped on Elevation Slopemap Flow (28,46) = 451 Paths Flow map draped on Elevation Flow map

18. Deriving Erosion Potential (terrain modeling) Erosion_potentialFlow/Slope Slope_classes Flow_classes But all buffer-feet are not the same… Need to reach farther under some conditions and not as far under others— common sense? Erosion Potential Simple Buffer – fixed geographic reach Protect the stream Flowmap Slopemap Individual Map Analysis Operations

19. Calculating Effective Distance (variable-width buffer) Erosion_potential Streams Erosion Buffers Distance away from the streams is a function of the erosion potential (Flow/Slope Class) with intervening heavy flow and steep slopes computed as effectively closer than simple distance— “as the crow walks” Effective Distance Variable-width Buffers Effective Erosion Distance CloseFar Heavy/Steep (far from stream) Light/Gentle (close) Simple Buffer (Berry)

20. Conclusions Where is What Mapping involves precise placement (delineation) of physical features (graphic) DescriptiveMapping (Berry)MultimediaMappingGISModeling Analysis involves investigation of spatial relationships (numerical)PrescriptiveModeling Why and So What Geotechnology promises to forever change how we conceptualize, utilize and visualize spatial relationships in scientific research and commercial applications Remote Sensing, GPS, Internet Mapping, Desktop Mapping, Multimedia Mapping, Spatial Statistics and Spatial Analysis

21. Where to go from here… GPS – Google Earth — and Beyond (# OSHR 1502 100 ) Thursdays October 9, 16, 23, 30 from 5:00 p.m. to 7:00 p.m. and Saturday field lab, October 25 and Saturday field lab, October 25 from 9:00 a.m. to 1:00 p.m. Osher Lifelong Learning Institute Colorado State University, Division of Continuing Education Phone: 303-376-2618 Web Site: http://www.learn.colostate.edu/fortcollins/osher/ http://www.learn.colostate.edu/fortcollins/osher/ www.innovativegis.com